#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
数据分析与报告系统 (Data Analytics & Reporting System)

主要功能：
1. 多维度数据分析和可视化
2. 自定义报告生成和调度
3. 关键绩效指标(KPI)监控
4. 预测分析和趋势预测

作者: Assistant
日期: 2024
"""

import json
import csv
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any, Tuple
from enum import Enum
from dataclasses import dataclass, asdict
import uuid
import statistics
import math
from collections import defaultdict, Counter

class ReportType(Enum):
    """报告类型"""
    INVENTORY = "inventory"
    SALES = "sales"
    PROCUREMENT = "procurement"
    SUPPLIER = "supplier"
    QUALITY = "quality"
    FINANCIAL = "financial"
    OPERATIONAL = "operational"
    CUSTOM = "custom"

class ReportFormat(Enum):
    """报告格式"""
    PDF = "pdf"
    EXCEL = "excel"
    CSV = "csv"
    JSON = "json"
    HTML = "html"

class ReportFrequency(Enum):
    """报告频率"""
    DAILY = "daily"
    WEEKLY = "weekly"
    MONTHLY = "monthly"
    QUARTERLY = "quarterly"
    YEARLY = "yearly"
    ON_DEMAND = "on_demand"

class ChartType(Enum):
    """图表类型"""
    LINE = "line"
    BAR = "bar"
    PIE = "pie"
    SCATTER = "scatter"
    HISTOGRAM = "histogram"
    HEATMAP = "heatmap"
    GAUGE = "gauge"
    TABLE = "table"

class KPIStatus(Enum):
    """KPI状态"""
    EXCELLENT = "excellent"
    GOOD = "good"
    WARNING = "warning"
    CRITICAL = "critical"

@dataclass
class DataSource:
    """数据源配置"""
    source_id: str
    name: str
    source_type: str  # database, api, file, etc.
    connection_config: Dict[str, Any]
    tables_entities: List[str]
    refresh_frequency: str
    last_updated: datetime
    is_active: bool = True
    
    def __post_init__(self):
        if self.last_updated is None:
            self.last_updated = datetime.now()

@dataclass
class KPIDefinition:
    """KPI定义"""
    kpi_id: str
    name: str
    description: str
    category: str
    calculation_formula: str
    target_value: float
    warning_threshold: float
    critical_threshold: float
    unit: str
    data_sources: List[str]
    calculation_frequency: str
    owner: str
    created_date: datetime
    is_active: bool = True
    
    def __post_init__(self):
        if self.created_date is None:
            self.created_date = datetime.now()

@dataclass
class KPIValue:
    """KPI值记录"""
    value_id: str
    kpi_id: str
    value: float
    target_value: float
    status: str
    calculation_date: datetime
    period_start: datetime
    period_end: datetime
    raw_data: Dict[str, Any]
    notes: str = ""
    
    def __post_init__(self):
        if self.calculation_date is None:
            self.calculation_date = datetime.now()

@dataclass
class ReportTemplate:
    """报告模板"""
    template_id: str
    name: str
    description: str
    report_type: str
    sections: List[Dict[str, Any]]
    parameters: Dict[str, Any]
    data_sources: List[str]
    created_by: str
    created_date: datetime
    last_modified: datetime
    is_active: bool = True
    
    def __post_init__(self):
        if self.created_date is None:
            self.created_date = datetime.now()
        if self.last_modified is None:
            self.last_modified = datetime.now()

@dataclass
class ReportSchedule:
    """报告调度"""
    schedule_id: str
    template_id: str
    name: str
    frequency: str
    recipients: List[str]
    format: str
    parameters: Dict[str, Any]
    next_run_time: datetime
    last_run_time: Optional[datetime]
    created_by: str
    is_active: bool = True
    
    def __post_init__(self):
        if self.last_run_time is None:
            self.last_run_time = None

@dataclass
class AnalysisResult:
    """分析结果"""
    analysis_id: str
    analysis_type: str
    title: str
    description: str
    data: Dict[str, Any]
    charts: List[Dict[str, Any]]
    insights: List[str]
    recommendations: List[str]
    created_date: datetime
    created_by: str
    
    def __post_init__(self):
        if self.created_date is None:
            self.created_date = datetime.now()

class DataAnalyticsReporting:
    """数据分析与报告系统"""
    
    def __init__(self):
        self.data_sources: Dict[str, DataSource] = {}
        self.kpi_definitions: Dict[str, KPIDefinition] = {}
        self.kpi_values: List[KPIValue] = []
        self.report_templates: Dict[str, ReportTemplate] = {}
        self.report_schedules: Dict[str, ReportSchedule] = {}
        self.analysis_results: Dict[str, AnalysisResult] = {}
        self.users: Dict[str, Dict[str, Any]] = {}
        
        # 模拟数据存储
        self.sample_data = self._initialize_sample_data()
    
    def register_data_source(self, source_data: Dict[str, Any]) -> bool:
        """注册数据源"""
        try:
            data_source = DataSource(
                source_id=source_data['source_id'],
                name=source_data['name'],
                source_type=source_data['source_type'],
                connection_config=source_data.get('connection_config', {}),
                tables_entities=source_data.get('tables_entities', []),
                refresh_frequency=source_data.get('refresh_frequency', 'daily'),
                last_updated=source_data.get('last_updated', datetime.now())
            )
            
            self.data_sources[data_source.source_id] = data_source
            return True
            
        except Exception as e:
            print(f"注册数据源失败: {e}")
            return False
    
    def create_kpi_definition(self, kpi_data: Dict[str, Any]) -> Optional[str]:
        """创建KPI定义"""
        try:
            kpi_id = f"KPI_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}"
            
            kpi_definition = KPIDefinition(
                kpi_id=kpi_id,
                name=kpi_data['name'],
                description=kpi_data['description'],
                category=kpi_data['category'],
                calculation_formula=kpi_data['calculation_formula'],
                target_value=kpi_data['target_value'],
                warning_threshold=kpi_data.get('warning_threshold', kpi_data['target_value'] * 0.8),
                critical_threshold=kpi_data.get('critical_threshold', kpi_data['target_value'] * 0.6),
                unit=kpi_data.get('unit', ''),
                data_sources=kpi_data.get('data_sources', []),
                calculation_frequency=kpi_data.get('calculation_frequency', 'daily'),
                owner=kpi_data['owner']
            )
            
            self.kpi_definitions[kpi_id] = kpi_definition
            return kpi_id
            
        except Exception as e:
            print(f"创建KPI定义失败: {e}")
            return None
    
    def calculate_kpi_value(self, kpi_id: str, period_start: datetime, period_end: datetime) -> Optional[str]:
        """计算KPI值"""
        if kpi_id not in self.kpi_definitions:
            return None
        
        try:
            kpi_def = self.kpi_definitions[kpi_id]
            
            # 模拟KPI计算（实际应用中需要从数据源获取数据并计算）
            calculated_value = self._simulate_kpi_calculation(kpi_def, period_start, period_end)
            
            # 确定状态
            status = self._determine_kpi_status(calculated_value, kpi_def)
            
            value_id = f"VAL_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}"
            
            kpi_value = KPIValue(
                value_id=value_id,
                kpi_id=kpi_id,
                value=calculated_value,
                target_value=kpi_def.target_value,
                status=status,
                period_start=period_start,
                period_end=period_end,
                raw_data={'calculation_method': 'simulated'}
            )
            
            self.kpi_values.append(kpi_value)
            return value_id
            
        except Exception as e:
            print(f"计算KPI值失败: {e}")
            return None
    
    def get_kpi_dashboard(self, category: str = None) -> Dict[str, Any]:
        """获取KPI仪表板"""
        try:
            # 筛选KPI
            if category:
                kpis = {k: v for k, v in self.kpi_definitions.items() if v.category == category and v.is_active}
            else:
                kpis = {k: v for k, v in self.kpi_definitions.items() if v.is_active}
            
            dashboard_data = {
                'summary': {
                    'total_kpis': len(kpis),
                    'excellent_count': 0,
                    'good_count': 0,
                    'warning_count': 0,
                    'critical_count': 0
                },
                'kpi_details': [],
                'trends': {},
                'alerts': []
            }
            
            for kpi_id, kpi_def in kpis.items():
                # 获取最新KPI值
                latest_values = [
                    v for v in self.kpi_values 
                    if v.kpi_id == kpi_id
                ]
                
                if latest_values:
                    latest_value = max(latest_values, key=lambda x: x.calculation_date)
                    
                    # 更新状态统计
                    dashboard_data['summary'][f'{latest_value.status}_count'] += 1
                    
                    # 获取趋势数据
                    trend_data = self._get_kpi_trend(kpi_id, days=30)
                    
                    kpi_detail = {
                        'kpi_id': kpi_id,
                        'name': kpi_def.name,
                        'category': kpi_def.category,
                        'current_value': latest_value.value,
                        'target_value': kpi_def.target_value,
                        'status': latest_value.status,
                        'unit': kpi_def.unit,
                        'last_updated': latest_value.calculation_date,
                        'trend': trend_data['trend'],
                        'change_percentage': trend_data['change_percentage']
                    }
                    
                    dashboard_data['kpi_details'].append(kpi_detail)
                    dashboard_data['trends'][kpi_id] = trend_data['values']
                    
                    # 添加警报
                    if latest_value.status in [KPIStatus.WARNING.value, KPIStatus.CRITICAL.value]:
                        dashboard_data['alerts'].append({
                            'kpi_id': kpi_id,
                            'kpi_name': kpi_def.name,
                            'status': latest_value.status,
                            'current_value': latest_value.value,
                            'target_value': kpi_def.target_value,
                            'message': f"{kpi_def.name}当前值{latest_value.value}，低于目标值{kpi_def.target_value}"
                        })
            
            # 按状态排序KPI详情
            status_order = {KPIStatus.CRITICAL.value: 0, KPIStatus.WARNING.value: 1, 
                          KPIStatus.GOOD.value: 2, KPIStatus.EXCELLENT.value: 3}
            dashboard_data['kpi_details'].sort(key=lambda x: status_order.get(x['status'], 4))
            
            return dashboard_data
            
        except Exception as e:
            print(f"获取KPI仪表板失败: {e}")
            return {'error': str(e)}
    
    def create_report_template(self, template_data: Dict[str, Any]) -> Optional[str]:
        """创建报告模板"""
        try:
            template_id = f"TPL_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}"
            
            template = ReportTemplate(
                template_id=template_id,
                name=template_data['name'],
                description=template_data.get('description', ''),
                report_type=template_data['report_type'],
                sections=template_data.get('sections', []),
                parameters=template_data.get('parameters', {}),
                data_sources=template_data.get('data_sources', []),
                created_by=template_data['created_by']
            )
            
            self.report_templates[template_id] = template
            return template_id
            
        except Exception as e:
            print(f"创建报告模板失败: {e}")
            return None
    
    def generate_report(self, template_id: str, parameters: Dict[str, Any] = None) -> Dict[str, Any]:
        """生成报告"""
        if template_id not in self.report_templates:
            return {'error': '报告模板不存在'}
        
        try:
            template = self.report_templates[template_id]
            report_params = {**template.parameters, **(parameters or {})}
            
            # 生成报告内容
            report_content = {
                'report_id': f"RPT_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}",
                'template_id': template_id,
                'title': template.name,
                'description': template.description,
                'generated_date': datetime.now(),
                'parameters': report_params,
                'sections': []
            }
            
            # 处理各个报告段落
            for section in template.sections:
                section_content = self._generate_report_section(section, report_params)
                report_content['sections'].append(section_content)
            
            # 生成执行摘要
            report_content['executive_summary'] = self._generate_executive_summary(report_content)
            
            return report_content
            
        except Exception as e:
            print(f"生成报告失败: {e}")
            return {'error': str(e)}
    
    def schedule_report(self, schedule_data: Dict[str, Any]) -> Optional[str]:
        """调度报告"""
        try:
            schedule_id = f"SCH_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}"
            
            # 计算下次运行时间
            next_run_time = self._calculate_next_run_time(
                schedule_data['frequency'],
                schedule_data.get('start_time', datetime.now())
            )
            
            schedule = ReportSchedule(
                schedule_id=schedule_id,
                template_id=schedule_data['template_id'],
                name=schedule_data['name'],
                frequency=schedule_data['frequency'],
                recipients=schedule_data.get('recipients', []),
                format=schedule_data.get('format', ReportFormat.PDF.value),
                parameters=schedule_data.get('parameters', {}),
                next_run_time=next_run_time,
                created_by=schedule_data['created_by']
            )
            
            self.report_schedules[schedule_id] = schedule
            return schedule_id
            
        except Exception as e:
            print(f"调度报告失败: {e}")
            return None
    
    def perform_multidimensional_analysis(self, analysis_config: Dict[str, Any]) -> Optional[str]:
        """执行多维度分析"""
        try:
            analysis_id = f"ANA_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}"
            
            # 获取分析数据
            analysis_data = self._get_analysis_data(analysis_config)
            
            # 执行分析
            analysis_results = self._perform_analysis(analysis_data, analysis_config)
            
            # 生成图表
            charts = self._generate_analysis_charts(analysis_results, analysis_config)
            
            # 生成洞察和建议
            insights = self._generate_insights(analysis_results)
            recommendations = self._generate_recommendations(analysis_results)
            
            result = AnalysisResult(
                analysis_id=analysis_id,
                analysis_type=analysis_config['analysis_type'],
                title=analysis_config.get('title', '多维度分析'),
                description=analysis_config.get('description', ''),
                data=analysis_results,
                charts=charts,
                insights=insights,
                recommendations=recommendations,
                created_by=analysis_config['created_by']
            )
            
            self.analysis_results[analysis_id] = result
            return analysis_id
            
        except Exception as e:
            print(f"执行多维度分析失败: {e}")
            return None
    
    def perform_predictive_analysis(self, prediction_config: Dict[str, Any]) -> Dict[str, Any]:
        """执行预测分析"""
        try:
            # 获取历史数据
            historical_data = self._get_historical_data(prediction_config)
            
            if not historical_data:
                return {'error': '没有足够的历史数据进行预测'}
            
            # 执行预测
            predictions = self._perform_prediction(historical_data, prediction_config)
            
            # 计算预测准确性指标
            accuracy_metrics = self._calculate_prediction_accuracy(historical_data, predictions)
            
            # 生成预测图表
            prediction_charts = self._generate_prediction_charts(historical_data, predictions)
            
            return {
                'prediction_id': f"PRED_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}",
                'prediction_type': prediction_config['prediction_type'],
                'target_metric': prediction_config['target_metric'],
                'prediction_period': prediction_config['prediction_period'],
                'historical_data_points': len(historical_data),
                'predictions': predictions,
                'accuracy_metrics': accuracy_metrics,
                'charts': prediction_charts,
                'confidence_level': prediction_config.get('confidence_level', 0.95),
                'generated_date': datetime.now(),
                'insights': self._generate_prediction_insights(predictions, accuracy_metrics)
            }
            
        except Exception as e:
            print(f"执行预测分析失败: {e}")
            return {'error': str(e)}
    
    def get_analytics_dashboard(self) -> Dict[str, Any]:
        """获取分析仪表板"""
        try:
            # 获取最近的分析结果
            recent_analyses = sorted(
                self.analysis_results.values(),
                key=lambda x: x.created_date,
                reverse=True
            )[:10]
            
            # 统计分析类型
            analysis_types = Counter([a.analysis_type for a in self.analysis_results.values()])
            
            # 获取数据源状态
            data_source_status = {
                'total_sources': len(self.data_sources),
                'active_sources': len([ds for ds in self.data_sources.values() if ds.is_active]),
                'last_updated': max([ds.last_updated for ds in self.data_sources.values()]) if self.data_sources else None
            }
            
            # 获取报告统计
            report_stats = {
                'total_templates': len(self.report_templates),
                'active_schedules': len([s for s in self.report_schedules.values() if s.is_active]),
                'total_kpis': len(self.kpi_definitions)
            }
            
            return {
                'overview': {
                    'total_analyses': len(self.analysis_results),
                    'recent_analyses_count': len(recent_analyses),
                    'data_sources': data_source_status,
                    'reports': report_stats
                },
                'recent_analyses': [
                    {
                        'analysis_id': a.analysis_id,
                        'title': a.title,
                        'analysis_type': a.analysis_type,
                        'created_date': a.created_date,
                        'insights_count': len(a.insights)
                    }
                    for a in recent_analyses
                ],
                'analysis_distribution': dict(analysis_types),
                'upcoming_reports': self._get_upcoming_reports(),
                'system_health': self._get_system_health_metrics()
            }
            
        except Exception as e:
            print(f"获取分析仪表板失败: {e}")
            return {'error': str(e)}
    
    def export_analysis_data(self, file_path: str, data_type: str = "analyses") -> bool:
        """导出分析数据"""
        try:
            if data_type == "analyses":
                data = [asdict(analysis) for analysis in self.analysis_results.values()]
            elif data_type == "kpis":
                data = [asdict(kpi) for kpi in self.kpi_definitions.values()]
            elif data_type == "kpi_values":
                data = [asdict(value) for value in self.kpi_values]
            elif data_type == "templates":
                data = [asdict(template) for template in self.report_templates.values()]
            elif data_type == "schedules":
                data = [asdict(schedule) for schedule in self.report_schedules.values()]
            else:
                return False
            
            if not data:
                return False
            
            # 导出为CSV格式
            with open(file_path, 'w', newline='', encoding='utf-8-sig') as csvfile:
                if data:
                    # 处理嵌套字典和列表
                    processed_data = []
                    for row in data:
                        processed_row = {}
                        for key, value in row.items():
                            if isinstance(value, datetime):
                                processed_row[key] = value.strftime('%Y-%m-%d %H:%M:%S')
                            elif isinstance(value, (list, dict)):
                                processed_row[key] = json.dumps(value, ensure_ascii=False)
                            else:
                                processed_row[key] = value
                        processed_data.append(processed_row)
                    
                    fieldnames = processed_data[0].keys()
                    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
                    writer.writeheader()
                    writer.writerows(processed_data)
            
            return True
            
        except Exception as e:
            print(f"导出分析数据失败: {e}")
            return False
    
    # 私有辅助方法
    def _initialize_sample_data(self) -> Dict[str, Any]:
        """初始化示例数据"""
        return {
            'inventory': {
                'total_items': 1500,
                'low_stock_items': 45,
                'out_of_stock_items': 12,
                'inventory_turnover': 6.2,
                'carrying_cost': 250000
            },
            'sales': {
                'monthly_revenue': 2500000,
                'order_count': 850,
                'average_order_value': 2941,
                'customer_satisfaction': 4.3,
                'return_rate': 0.05
            },
            'procurement': {
                'total_suppliers': 120,
                'active_suppliers': 95,
                'average_lead_time': 15,
                'cost_savings': 180000,
                'supplier_performance': 0.87
            },
            'quality': {
                'defect_rate': 0.02,
                'first_pass_yield': 0.96,
                'customer_complaints': 8,
                'quality_cost': 45000,
                'audit_score': 92
            }
        }
    
    def _simulate_kpi_calculation(self, kpi_def: KPIDefinition, period_start: datetime, period_end: datetime) -> float:
        """模拟KPI计算"""
        # 基于KPI类别生成模拟值
        base_value = kpi_def.target_value
        
        if kpi_def.category == 'inventory':
            # 库存相关KPI，添加一些随机波动
            variation = base_value * 0.1 * (0.5 - statistics.random())
            return max(0, base_value + variation)
        elif kpi_def.category == 'sales':
            # 销售相关KPI，通常有季节性波动
            month = period_start.month
            seasonal_factor = 1.0 + 0.2 * math.sin(2 * math.pi * month / 12)
            variation = base_value * 0.15 * (0.5 - statistics.random())
            return max(0, base_value * seasonal_factor + variation)
        elif kpi_def.category == 'quality':
            # 质量相关KPI，通常比较稳定
            variation = base_value * 0.05 * (0.5 - statistics.random())
            return max(0, base_value + variation)
        else:
            # 其他KPI，一般波动
            variation = base_value * 0.12 * (0.5 - statistics.random())
            return max(0, base_value + variation)
    
    def _determine_kpi_status(self, value: float, kpi_def: KPIDefinition) -> str:
        """确定KPI状态"""
        if value >= kpi_def.target_value:
            return KPIStatus.EXCELLENT.value
        elif value >= kpi_def.warning_threshold:
            return KPIStatus.GOOD.value
        elif value >= kpi_def.critical_threshold:
            return KPIStatus.WARNING.value
        else:
            return KPIStatus.CRITICAL.value
    
    def _get_kpi_trend(self, kpi_id: str, days: int = 30) -> Dict[str, Any]:
        """获取KPI趋势"""
        cutoff_date = datetime.now() - timedelta(days=days)
        
        kpi_values = [
            v for v in self.kpi_values
            if v.kpi_id == kpi_id and v.calculation_date >= cutoff_date
        ]
        
        if len(kpi_values) < 2:
            return {'trend': 'stable', 'change_percentage': 0.0, 'values': []}
        
        # 按时间排序
        kpi_values.sort(key=lambda x: x.calculation_date)
        
        # 计算趋势
        values = [v.value for v in kpi_values]
        first_value = values[0]
        last_value = values[-1]
        
        change_percentage = ((last_value - first_value) / first_value * 100) if first_value != 0 else 0
        
        if change_percentage > 5:
            trend = 'increasing'
        elif change_percentage < -5:
            trend = 'decreasing'
        else:
            trend = 'stable'
        
        return {
            'trend': trend,
            'change_percentage': change_percentage,
            'values': [{'date': v.calculation_date, 'value': v.value} for v in kpi_values]
        }
    
    def _generate_report_section(self, section: Dict[str, Any], parameters: Dict[str, Any]) -> Dict[str, Any]:
        """生成报告段落"""
        section_type = section.get('type', 'text')
        
        if section_type == 'kpi_summary':
            return self._generate_kpi_summary_section(section, parameters)
        elif section_type == 'chart':
            return self._generate_chart_section(section, parameters)
        elif section_type == 'table':
            return self._generate_table_section(section, parameters)
        elif section_type == 'analysis':
            return self._generate_analysis_section(section, parameters)
        else:
            return {
                'type': section_type,
                'title': section.get('title', ''),
                'content': section.get('content', ''),
                'data': {}
            }
    
    def _generate_kpi_summary_section(self, section: Dict[str, Any], parameters: Dict[str, Any]) -> Dict[str, Any]:
        """生成KPI摘要段落"""
        category = section.get('category')
        kpi_data = self.get_kpi_dashboard(category)
        
        return {
            'type': 'kpi_summary',
            'title': section.get('title', 'KPI摘要'),
            'content': f"本期共监控{kpi_data['summary']['total_kpis']}个KPI指标",
            'data': kpi_data
        }
    
    def _generate_chart_section(self, section: Dict[str, Any], parameters: Dict[str, Any]) -> Dict[str, Any]:
        """生成图表段落"""
        chart_type = section.get('chart_type', 'bar')
        data_source = section.get('data_source', 'sample')
        
        # 模拟图表数据
        chart_data = self._get_chart_data(data_source, chart_type, parameters)
        
        return {
            'type': 'chart',
            'title': section.get('title', '图表'),
            'chart_type': chart_type,
            'data': chart_data,
            'config': section.get('config', {})
        }
    
    def _generate_table_section(self, section: Dict[str, Any], parameters: Dict[str, Any]) -> Dict[str, Any]:
        """生成表格段落"""
        data_source = section.get('data_source', 'sample')
        
        # 模拟表格数据
        table_data = self._get_table_data(data_source, parameters)
        
        return {
            'type': 'table',
            'title': section.get('title', '数据表格'),
            'headers': table_data.get('headers', []),
            'rows': table_data.get('rows', []),
            'config': section.get('config', {})
        }
    
    def _generate_analysis_section(self, section: Dict[str, Any], parameters: Dict[str, Any]) -> Dict[str, Any]:
        """生成分析段落"""
        analysis_type = section.get('analysis_type', 'trend')
        
        # 模拟分析结果
        analysis_data = self._perform_section_analysis(analysis_type, parameters)
        
        return {
            'type': 'analysis',
            'title': section.get('title', '数据分析'),
            'analysis_type': analysis_type,
            'insights': analysis_data.get('insights', []),
            'recommendations': analysis_data.get('recommendations', []),
            'data': analysis_data.get('data', {})
        }
    
    def _generate_executive_summary(self, report_content: Dict[str, Any]) -> Dict[str, Any]:
        """生成执行摘要"""
        # 从各个段落中提取关键信息
        key_insights = []
        key_metrics = {}
        recommendations = []
        
        for section in report_content['sections']:
            if section['type'] == 'kpi_summary':
                kpi_data = section['data']
                key_metrics['kpi_summary'] = kpi_data['summary']
                
                # 添加关键洞察
                if kpi_data['summary']['critical_count'] > 0:
                    key_insights.append(f"发现{kpi_data['summary']['critical_count']}个严重KPI指标需要立即关注")
                
            elif section['type'] == 'analysis':
                key_insights.extend(section.get('insights', [])[:2])  # 取前2个洞察
                recommendations.extend(section.get('recommendations', [])[:2])  # 取前2个建议
        
        return {
            'key_insights': key_insights[:5],  # 最多5个关键洞察
            'key_metrics': key_metrics,
            'recommendations': recommendations[:5],  # 最多5个建议
            'overall_status': self._determine_overall_status(key_metrics)
        }
    
    def _calculate_next_run_time(self, frequency: str, start_time: datetime) -> datetime:
        """计算下次运行时间"""
        if frequency == ReportFrequency.DAILY.value:
            return start_time + timedelta(days=1)
        elif frequency == ReportFrequency.WEEKLY.value:
            return start_time + timedelta(weeks=1)
        elif frequency == ReportFrequency.MONTHLY.value:
            # 简化处理，加30天
            return start_time + timedelta(days=30)
        elif frequency == ReportFrequency.QUARTERLY.value:
            return start_time + timedelta(days=90)
        elif frequency == ReportFrequency.YEARLY.value:
            return start_time + timedelta(days=365)
        else:
            return start_time + timedelta(days=1)  # 默认每日
    
    def _get_analysis_data(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """获取分析数据"""
        # 模拟从数据源获取数据
        data_sources = config.get('data_sources', ['sample'])
        analysis_data = {}
        
        for source in data_sources:
            if source in self.sample_data:
                analysis_data[source] = self.sample_data[source]
            else:
                # 生成模拟数据
                analysis_data[source] = self._generate_mock_data(source)
        
        return analysis_data
    
    def _perform_analysis(self, data: Dict[str, Any], config: Dict[str, Any]) -> Dict[str, Any]:
        """执行分析"""
        analysis_type = config.get('analysis_type', 'descriptive')
        
        if analysis_type == 'descriptive':
            return self._perform_descriptive_analysis(data)
        elif analysis_type == 'comparative':
            return self._perform_comparative_analysis(data, config)
        elif analysis_type == 'correlation':
            return self._perform_correlation_analysis(data)
        elif analysis_type == 'trend':
            return self._perform_trend_analysis(data, config)
        else:
            return {'analysis_type': analysis_type, 'results': data}
    
    def _perform_descriptive_analysis(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """执行描述性分析"""
        results = {
            'analysis_type': 'descriptive',
            'summary_statistics': {},
            'distributions': {},
            'outliers': {}
        }
        
        for source, source_data in data.items():
            if isinstance(source_data, dict):
                numeric_fields = {k: v for k, v in source_data.items() if isinstance(v, (int, float))}
                
                if numeric_fields:
                    values = list(numeric_fields.values())
                    results['summary_statistics'][source] = {
                        'mean': statistics.mean(values),
                        'median': statistics.median(values),
                        'std_dev': statistics.stdev(values) if len(values) > 1 else 0,
                        'min': min(values),
                        'max': max(values),
                        'count': len(values)
                    }
        
        return results
    
    def _perform_comparative_analysis(self, data: Dict[str, Any], config: Dict[str, Any]) -> Dict[str, Any]:
        """执行比较分析"""
        compare_fields = config.get('compare_fields', [])
        
        results = {
            'analysis_type': 'comparative',
            'comparisons': {},
            'rankings': {},
            'variance_analysis': {}
        }
        
        # 模拟比较分析
        for field in compare_fields:
            field_values = []
            for source, source_data in data.items():
                if isinstance(source_data, dict) and field in source_data:
                    field_values.append({'source': source, 'value': source_data[field]})
            
            if field_values:
                # 排序
                field_values.sort(key=lambda x: x['value'], reverse=True)
                results['rankings'][field] = field_values
                
                # 计算方差
                values = [item['value'] for item in field_values]
                if len(values) > 1:
                    results['variance_analysis'][field] = {
                        'variance': statistics.variance(values),
                        'coefficient_of_variation': statistics.stdev(values) / statistics.mean(values)
                    }
        
        return results
    
    def _perform_correlation_analysis(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """执行相关性分析"""
        results = {
            'analysis_type': 'correlation',
            'correlations': {},
            'strong_correlations': []
        }
        
        # 模拟相关性分析
        all_numeric_data = {}
        for source, source_data in data.items():
            if isinstance(source_data, dict):
                for key, value in source_data.items():
                    if isinstance(value, (int, float)):
                        all_numeric_data[f"{source}_{key}"] = value
        
        # 简化的相关性计算（实际应用中需要更复杂的算法）
        fields = list(all_numeric_data.keys())
        for i, field1 in enumerate(fields):
            for field2 in fields[i+1:]:
                # 模拟相关系数
                correlation = (hash(field1 + field2) % 200 - 100) / 100.0
                results['correlations'][f"{field1}_vs_{field2}"] = correlation
                
                if abs(correlation) > 0.7:
                    results['strong_correlations'].append({
                        'field1': field1,
                        'field2': field2,
                        'correlation': correlation,
                        'strength': 'strong' if abs(correlation) > 0.8 else 'moderate'
                    })
        
        return results
    
    def _perform_trend_analysis(self, data: Dict[str, Any], config: Dict[str, Any]) -> Dict[str, Any]:
        """执行趋势分析"""
        results = {
            'analysis_type': 'trend',
            'trends': {},
            'forecasts': {},
            'seasonality': {}
        }
        
        # 模拟趋势分析
        for source, source_data in data.items():
            if isinstance(source_data, dict):
                for field, value in source_data.items():
                    if isinstance(value, (int, float)):
                        # 模拟趋势
                        trend_direction = 'increasing' if hash(field) % 3 == 0 else 'decreasing' if hash(field) % 3 == 1 else 'stable'
                        trend_strength = (hash(field) % 100) / 100.0
                        
                        results['trends'][f"{source}_{field}"] = {
                            'direction': trend_direction,
                            'strength': trend_strength,
                            'current_value': value
                        }
        
        return results
    
    def _generate_analysis_charts(self, results: Dict[str, Any], config: Dict[str, Any]) -> List[Dict[str, Any]]:
        """生成分析图表"""
        charts = []
        analysis_type = results.get('analysis_type', 'descriptive')
        
        if analysis_type == 'descriptive':
            # 生成描述性统计图表
            if 'summary_statistics' in results:
                charts.append({
                    'type': ChartType.BAR.value,
                    'title': '描述性统计摘要',
                    'data': results['summary_statistics'],
                    'config': {'x_axis': 'metrics', 'y_axis': 'values'}
                })
        
        elif analysis_type == 'comparative':
            # 生成比较分析图表
            if 'rankings' in results:
                for field, rankings in results['rankings'].items():
                    charts.append({
                        'type': ChartType.BAR.value,
                        'title': f'{field}比较分析',
                        'data': rankings,
                        'config': {'x_axis': 'source', 'y_axis': 'value'}
                    })
        
        elif analysis_type == 'correlation':
            # 生成相关性分析图表
            if 'strong_correlations' in results:
                charts.append({
                    'type': ChartType.SCATTER.value,
                    'title': '强相关性分析',
                    'data': results['strong_correlations'],
                    'config': {'show_correlation_line': True}
                })
        
        elif analysis_type == 'trend':
            # 生成趋势分析图表
            if 'trends' in results:
                charts.append({
                    'type': ChartType.LINE.value,
                    'title': '趋势分析',
                    'data': results['trends'],
                    'config': {'show_trend_line': True}
                })
        
        return charts
    
    def _generate_insights(self, results: Dict[str, Any]) -> List[str]:
        """生成洞察"""
        insights = []
        analysis_type = results.get('analysis_type', 'descriptive')
        
        if analysis_type == 'descriptive':
            if 'summary_statistics' in results:
                for source, stats in results['summary_statistics'].items():
                    if stats['std_dev'] > stats['mean'] * 0.3:
                        insights.append(f"{source}数据波动较大，标准差为{stats['std_dev']:.2f}")
                    
                    if stats['max'] > stats['mean'] * 2:
                        insights.append(f"{source}存在异常高值，最大值为{stats['max']:.2f}")
        
        elif analysis_type == 'comparative':
            if 'rankings' in results:
                for field, rankings in results['rankings'].items():
                    if len(rankings) >= 2:
                        top_performer = rankings[0]
                        bottom_performer = rankings[-1]
                        performance_gap = top_performer['value'] - bottom_performer['value']
                        insights.append(f"{field}指标中，{top_performer['source']}表现最佳，与最差的{bottom_performer['source']}相差{performance_gap:.2f}")
        
        elif analysis_type == 'correlation':
            if 'strong_correlations' in results:
                for corr in results['strong_correlations']:
                    correlation_type = '正相关' if corr['correlation'] > 0 else '负相关'
                    insights.append(f"{corr['field1']}与{corr['field2']}存在{corr['strength']}{correlation_type}（相关系数：{corr['correlation']:.2f}）")
        
        elif analysis_type == 'trend':
            if 'trends' in results:
                increasing_trends = [k for k, v in results['trends'].items() if v['direction'] == 'increasing']
                decreasing_trends = [k for k, v in results['trends'].items() if v['direction'] == 'decreasing']
                
                if increasing_trends:
                    insights.append(f"发现{len(increasing_trends)}个指标呈上升趋势")
                if decreasing_trends:
                    insights.append(f"发现{len(decreasing_trends)}个指标呈下降趋势")
        
        return insights[:5]  # 返回最多5个洞察
    
    def _generate_recommendations(self, results: Dict[str, Any]) -> List[str]:
        """生成建议"""
        recommendations = []
        analysis_type = results.get('analysis_type', 'descriptive')
        
        if analysis_type == 'descriptive':
            if 'summary_statistics' in results:
                for source, stats in results['summary_statistics'].items():
                    if stats['std_dev'] > stats['mean'] * 0.3:
                        recommendations.append(f"建议调查{source}数据波动的原因，制定稳定性改进措施")
        
        elif analysis_type == 'comparative':
            if 'rankings' in results:
                for field, rankings in results['rankings'].items():
                    if len(rankings) >= 2:
                        bottom_performer = rankings[-1]
                        recommendations.append(f"建议{bottom_performer['source']}学习最佳实践，提升{field}指标")
        
        elif analysis_type == 'correlation':
            if 'strong_correlations' in results:
                for corr in results['strong_correlations']:
                    if corr['correlation'] > 0.7:
                        recommendations.append(f"可以利用{corr['field1']}与{corr['field2']}的强正相关关系进行联合优化")
        
        elif analysis_type == 'trend':
            if 'trends' in results:
                decreasing_trends = [k for k, v in results['trends'].items() if v['direction'] == 'decreasing' and v['strength'] > 0.5]
                if decreasing_trends:
                    recommendations.append(f"建议重点关注下降趋势明显的指标，制定改进计划")
        
        return recommendations[:5]  # 返回最多5个建议
    
    def _get_historical_data(self, config: Dict[str, Any]) -> List[Dict[str, Any]]:
        """获取历史数据"""
        # 模拟历史数据
        target_metric = config.get('target_metric', 'revenue')
        days_back = config.get('historical_days', 90)
        
        historical_data = []
        base_value = 100000  # 基础值
        
        for i in range(days_back):
            date = datetime.now() - timedelta(days=days_back - i)
            
            # 添加趋势和随机波动
            trend_factor = 1 + (i / days_back) * 0.1  # 轻微上升趋势
            seasonal_factor = 1 + 0.1 * math.sin(2 * math.pi * i / 30)  # 月度季节性
            random_factor = 1 + 0.05 * (0.5 - hash(str(date)) % 100 / 100)
            
            value = base_value * trend_factor * seasonal_factor * random_factor
            
            historical_data.append({
                'date': date,
                'value': value,
                'metric': target_metric
            })
        
        return historical_data
    
    def _perform_prediction(self, historical_data: List[Dict[str, Any]], config: Dict[str, Any]) -> List[Dict[str, Any]]:
        """执行预测"""
        prediction_days = config.get('prediction_days', 30)
        
        # 简单的线性回归预测
        values = [item['value'] for item in historical_data]
        n = len(values)
        
        # 计算趋势
        x_values = list(range(n))
        sum_x = sum(x_values)
        sum_y = sum(values)
        sum_xy = sum(x_values[i] * values[i] for i in range(n))
        sum_x2 = sum(x * x for x in x_values)
        
        # 线性回归系数
        slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x)
        intercept = (sum_y - slope * sum_x) / n
        
        # 生成预测
        predictions = []
        last_date = historical_data[-1]['date']
        
        for i in range(1, prediction_days + 1):
            pred_date = last_date + timedelta(days=i)
            pred_value = intercept + slope * (n + i)
            
            # 添加置信区间
            confidence_interval = pred_value * 0.1  # 简化的置信区间
            
            predictions.append({
                'date': pred_date,
                'predicted_value': pred_value,
                'lower_bound': pred_value - confidence_interval,
                'upper_bound': pred_value + confidence_interval,
                'confidence_level': config.get('confidence_level', 0.95)
            })
        
        return predictions
    
    def _calculate_prediction_accuracy(self, historical_data: List[Dict[str, Any]], predictions: List[Dict[str, Any]]) -> Dict[str, float]:
        """计算预测准确性"""
        # 模拟准确性指标
        return {
            'mae': 5234.56,  # 平均绝对误差
            'mse': 27891234.78,  # 均方误差
            'rmse': 5281.23,  # 均方根误差
            'mape': 0.052,  # 平均绝对百分比误差
            'r_squared': 0.87  # 决定系数
        }
    
    def _generate_prediction_charts(self, historical_data: List[Dict[str, Any]], predictions: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """生成预测图表"""
        return [
            {
                'type': ChartType.LINE.value,
                'title': '历史数据与预测',
                'data': {
                    'historical': historical_data,
                    'predictions': predictions
                },
                'config': {
                    'show_confidence_interval': True,
                    'x_axis': 'date',
                    'y_axis': 'value'
                }
            }
        ]
    
    def _generate_prediction_insights(self, predictions: List[Dict[str, Any]], accuracy_metrics: Dict[str, float]) -> List[str]:
        """生成预测洞察"""
        insights = []
        
        if predictions:
            first_pred = predictions[0]['predicted_value']
            last_pred = predictions[-1]['predicted_value']
            
            change_rate = (last_pred - first_pred) / first_pred * 100
            
            if change_rate > 5:
                insights.append(f"预测期内指标将上升{change_rate:.1f}%")
            elif change_rate < -5:
                insights.append(f"预测期内指标将下降{abs(change_rate):.1f}%")
            else:
                insights.append("预测期内指标将保持相对稳定")
        
        if accuracy_metrics['r_squared'] > 0.8:
            insights.append(f"预测模型拟合度较好（R²={accuracy_metrics['r_squared']:.2f}）")
        elif accuracy_metrics['r_squared'] < 0.6:
            insights.append(f"预测模型拟合度一般（R²={accuracy_metrics['r_squared']:.2f}），建议增加更多特征")
        
        return insights
    
    def _get_upcoming_reports(self) -> List[Dict[str, Any]]:
        """获取即将到来的报告"""
        upcoming = []
        current_time = datetime.now()
        
        for schedule in self.report_schedules.values():
            if schedule.is_active and schedule.next_run_time > current_time:
                template = self.report_templates.get(schedule.template_id)
                upcoming.append({
                    'schedule_id': schedule.schedule_id,
                    'name': schedule.name,
                    'template_name': template.name if template else 'Unknown',
                    'next_run_time': schedule.next_run_time,
                    'frequency': schedule.frequency,
                    'recipients_count': len(schedule.recipients)
                })
        
        # 按时间排序
        upcoming.sort(key=lambda x: x['next_run_time'])
        return upcoming[:5]  # 返回最近5个
    
    def _get_system_health_metrics(self) -> Dict[str, Any]:
        """获取系统健康指标"""
        return {
            'data_freshness': 'good',  # 数据新鲜度
            'processing_speed': 'normal',  # 处理速度
            'error_rate': 0.02,  # 错误率
            'uptime': 0.999,  # 运行时间
            'storage_usage': 0.65  # 存储使用率
        }
    
    def _get_chart_data(self, data_source: str, chart_type: str, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """获取图表数据"""
        # 模拟图表数据
        if chart_type == ChartType.BAR.value:
            return {
                'labels': ['Q1', 'Q2', 'Q3', 'Q4'],
                'datasets': [{
                    'label': '销售额',
                    'data': [2500000, 2800000, 3200000, 2900000]
                }]
            }
        elif chart_type == ChartType.LINE.value:
            return {
                'labels': ['1月', '2月', '3月', '4月', '5月', '6月'],
                'datasets': [{
                    'label': '库存周转率',
                    'data': [5.2, 5.8, 6.1, 5.9, 6.3, 6.0]
                }]
            }
        elif chart_type == ChartType.PIE.value:
            return {
                'labels': ['A类供应商', 'B类供应商', 'C类供应商'],
                'data': [45, 35, 20]
            }
        else:
            return {'message': '暂无数据'}
    
    def _get_table_data(self, data_source: str, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """获取表格数据"""
        # 模拟表格数据
        return {
            'headers': ['供应商', '评分', '交付准时率', '质量评分', '成本节约'],
            'rows': [
                ['供应商A', 95, '98%', 92, '15%'],
                ['供应商B', 88, '95%', 89, '12%'],
                ['供应商C', 92, '97%', 94, '18%'],
                ['供应商D', 85, '93%', 87, '10%']
            ]
        }
    
    def _perform_section_analysis(self, analysis_type: str, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """执行段落分析"""
        if analysis_type == 'trend':
            return {
                'insights': [
                    '销售额呈现稳定增长趋势',
                    '库存周转率有所提升',
                    '客户满意度保持高位'
                ],
                'recommendations': [
                    '继续保持当前增长策略',
                    '优化库存管理流程',
                    '加强客户关系维护'
                ],
                'data': {'trend_direction': 'positive', 'growth_rate': 0.15}
            }
        elif analysis_type == 'performance':
            return {
                'insights': [
                    '整体绩效指标达到预期',
                    '部分指标存在改进空间'
                ],
                'recommendations': [
                    '制定针对性改进计划',
                    '加强绩效监控频率'
                ],
                'data': {'overall_score': 85, 'improvement_areas': ['效率', '成本控制']}
            }
        else:
            return {
                'insights': ['数据分析完成'],
                'recommendations': ['持续监控关键指标'],
                'data': {}
            }
    
    def _determine_overall_status(self, key_metrics: Dict[str, Any]) -> str:
        """确定整体状态"""
        if 'kpi_summary' in key_metrics:
            kpi_summary = key_metrics['kpi_summary']
            critical_count = kpi_summary.get('critical_count', 0)
            warning_count = kpi_summary.get('warning_count', 0)
            
            if critical_count > 0:
                return 'critical'
            elif warning_count > 2:
                return 'warning'
            elif kpi_summary.get('excellent_count', 0) > kpi_summary.get('total_kpis', 1) * 0.7:
                return 'excellent'
            else:
                return 'good'
        
        return 'unknown'
    
    def _generate_mock_data(self, source: str) -> Dict[str, Any]:
        """生成模拟数据"""
        mock_data_templates = {
            'inventory': {
                'total_items': 1200 + hash(source) % 500,
                'low_stock_items': 30 + hash(source) % 20,
                'inventory_value': 500000 + hash(source) % 200000,
                'turnover_rate': 5.5 + (hash(source) % 20) / 10
            },
            'sales': {
                'revenue': 2000000 + hash(source) % 1000000,
                'orders': 800 + hash(source) % 200,
                'customers': 150 + hash(source) % 50,
                'conversion_rate': 0.15 + (hash(source) % 10) / 100
            },
            'procurement': {
                'suppliers': 80 + hash(source) % 40,
                'purchase_orders': 200 + hash(source) % 100,
                'cost_savings': 100000 + hash(source) % 50000,
                'lead_time': 12 + hash(source) % 8
            }
        }
        
        return mock_data_templates.get(source, {
            'value1': hash(source) % 1000,
            'value2': (hash(source) * 2) % 500,
            'value3': (hash(source) * 3) % 200
        })